Answer / Nand Nandan Dubey

Convolutional Neural Networks (CNN) and Recurrent Neural Networks (RNN) are two popular types of neural networks. CNNs are primarily used for image processing tasks, as they are designed to process data with a grid-like structure (e.g., images). They use convolution operations, pooling, and fully connected layers to learn spatial hierarchies and features in the input data. In contrast, RNNs are suited for sequence data and language modeling because of their ability to maintain an internal state that helps them remember previous inputs. However, they suffer from issues like vanishing gradients and exploding gradients, making long-term dependency learning challenging.

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